SlimStampen Usage During Lockdown
Maarten van der Velde
Last updated: 2021-01-29
Setup
library(data.table)
library(DBI)
library(ggplot2)
library(cowplot)
Attaching package: 'cowplot'The following object is masked from 'package:ggplot2':
ggsavelibrary(grid)
Sys.setlocale("LC_TIME", "en_US.UTF-8") # Print English date format[1] "en_US.UTF-8"en_US.UTF-8# Sys.setlocale("LC_TIME", "nl_NL.UTF-8") # Print Dutch date format
number_format <- scales::number_format(big.mark = ",", decimal.mark = ".") # Print English number format
# number_format <- scales::number_format(big.mark = ".", decimal.mark = ",") # Print Dutch number format
theme_paper <- theme_classic(base_size = 12) +
theme(axis.text = element_text(colour = "black"),
panel.grid.major.y = element_line(colour = "grey92"))School closure and opening dates
Sources: - https://www.rijksoverheid.nl/actueel/nieuws/2020/03/15/aanvullende-maatregelen-onderwijs-horeca-sport - https://www.rijksoverheid.nl/actueel/nieuws/2020/05/19/onderwijs-gaat-stap-voor-stap-open
date_schools_closed <- as.POSIXct("2020-03-16")
date_schools_opened <- as.POSIXct("2020-06-02")Handle database connections
db_connect <- function() {
db <- dbConnect(RSQLite::SQLite(), file.path("..", "data", "noordhoff.sqlite"))
return(db)
}
db_disconnect <- function(db) {
dbDisconnect(db)
}Data
The database contains all SlimStampen data collected via Noordhoff’s platform in three courses: Stepping Stones (English), Grandes Lignes (French), and Neue Kontakte (German).
Trial-level response data are stored in the responses table. Book information, such as the course year, book title, and chapter, are stored in the book_info table.
responses
| Column | Type | Explanation |
|---|---|---|
| date | int | UNIX time stamp [s] |
| user_id | chr | unique user identifier |
| method | chr | course |
| start_time | int | elapsed time since session start [ms] |
| rt | int | response time [ms] |
| duration | int | trial duration [ms] |
| fact_id | int | unique fact identifier (within chapter) |
| correct | int | response accuracy |
| answer | chr | user’s response |
| choices | int | number of answer choices (1 == open response) |
| backspace_used | dbl | user pressed backspace during trial |
| backspace_used_first | dbl | user erased first character of response |
| study | int | trial was a study trial |
| answer_language | chr | language of the answer |
| subsession | int | identifies part within learning session |
| book_info_id | chr | unique identifier of book information |
book_info
| Column | Type | Explanation |
|---|---|---|
| book_info_id | chr | unique identifier of book information |
| method_group | chr | year and edition |
| book_title | chr | book title (incl. year, level, edition) |
| book_type | chr | type of book |
| chapter | chr | chapter number and title |
Preview first 10 rows
db <- db_connect()
responses_top <- dbGetQuery(db, "SELECT * FROM responses LIMIT 10")
responses_topbook_info_top <- dbGetQuery(db, "SELECT * FROM book_info LIMIT 10")
book_info_topdb_disconnect(db)Usage
Get number of trials by method, day, and user:
db <- db_connect()
counts <- dbGetQuery(db,"SELECT responses.method AS 'method',
DATE(responses.date + 3600, 'unixepoch') AS 'doy',
responses.user_id AS 'user',
COUNT(*) AS 'trials'
FROM 'responses'
GROUP BY responses.method,
DATE(responses.date + 3600, 'unixepoch'),
responses.user_id
")
db_disconnect(db)
setDT(counts)Add a school year column (cutoff date: 1 August):
counts[, doy_posix := as.POSIXct(doy)]counts[, school_year := ifelse(doy_posix < "2019-08-01", "18/19", "19/20")]Add more sensible course names:
counts[, course := ifelse(method == "Grandes Lignes", "French", ifelse(method == "Stepping Stones", "English", "German"))]Total number of unique users by course
counts[, .(unique_users = length(unique(user))), by = .(course, school_year)]Total number of trials by course
counts[, .(total_trials = sum(trials)), by = .(course, school_year)]Number of trials by user
counts[, trials_user := sum(trials), by = .(course, user)]ggplot(counts, aes(x = trials_user)) +
facet_wrap(~course, ncol = 1, scales = "free_y") +
geom_histogram(binwidth = 100) +
labs(x = "Number of trials by user",
y = NULL) +
theme_paper
Number of unique days by user
ggplot(counts[, .(N = length(unique(doy_posix))), by = c("user", "course")], aes(x = N)) +
facet_grid(course ~ ., scales = "free_y") +
geom_histogram(binwidth = 1) +
geom_vline(xintercept = (1:7)*7, lty = 2, alpha = 0.5) +
labs(x = "Number of unique days by user",
y = NULL) +
theme_paper
Total number of trials by day
Interpolate missing days:
doy_posix <- seq.POSIXt(from = counts[,min(doy_posix)], to = counts[,max(doy_posix)], by = "DSTday")
course <- counts[,unique(course)]
dates <- CJ(doy_posix, course)
counts <- merge(counts, dates, by = c("doy_posix", "course"), all = TRUE)Count trials by day:
counts[, trials_total := sum(trials, na.rm = TRUE), by = .(course, doy_posix)]counts_by_day <- counts[, .(trials_total = sum(trials, na.rm = TRUE)), by = .(course, doy_posix)]ggplot(counts_by_day[course %in% c("English", "French"),],
aes(x = doy_posix, y = trials_total, colour = course)) +
geom_line() +
scale_x_datetime(date_breaks = "3 months", date_labels = "%e %b %Y") +
scale_y_continuous(labels = number_format) +
labs(x = NULL,
y = "Number of trials per day",
colour = "Course") +
theme_paper
Total number of trials by week
Use cut.Date() to bin dates by week. Each day is assigned the date of the most recent Monday.
counts_by_day[, doy_posix_week := cut.POSIXt(doy_posix, "week")]counts_by_day[, trials_total_week := sum(trials_total, na.rm = TRUE), by = .(course, doy_posix_week)]ggplot(counts_by_day[course %in% c("English", "French"),],
aes(x = doy_posix, y = trials_total_week, colour = course)) +
geom_line() +
scale_x_datetime(date_breaks = "3 months", date_labels = "%e %b %Y") +
scale_y_continuous(labels = number_format) +
labs(x = NULL,
y = "Number of trials per week",
colour = "Course") +
theme_paper
Overlap the two school years:
counts_by_day[, school_year := ifelse(doy_posix < "2019-08-01", "18/19", "19/20")]counts_by_day[school_year == "18/19", doy_posix_aligned := as.POSIXct(doy_posix + 365*24*60*60, origin = "1970-01-01")]counts_by_day[school_year == "19/20", doy_posix_aligned := doy_posix]p_trial_hist <- ggplot(counts_by_day[course %in% c("English", "French"),],
aes(x = doy_posix_aligned, ymin = 0, ymax = trials_total_week, group = school_year, colour = school_year, fill = school_year)) +
facet_wrap(~ course, ncol = 1) +
geom_rect(xmin = date_schools_closed, xmax = date_schools_opened, ymin = -2e5, ymax = 2.2e6, fill = "grey92", colour = "grey50", lty = 2) +
geom_ribbon(alpha = .2) +
scale_x_datetime(expand = c(0, 0),
breaks = as.POSIXct(c(
"2019-09-01 02:00:00 CET",
"2019-11-01 02:00:00 CET",
"2020-01-01 02:00:00 CET",
"2020-03-01 02:00:00 CET",
"2020-05-01 02:00:00 CET",
"2020-07-01 02:00:00 CET")),
limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
date_labels = "%b") +
scale_y_continuous(expand = c(0, 0), limits = c(0, 2e6), labels = number_format) +
scale_colour_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
scale_fill_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
labs(x = NULL,
y = "Trials per week",
colour = "School year",
fill = "School year") +
theme_paper
p_trial_hist
ggsave("../output/trial_hist.pdf", width = 5, height = 3)
ggsave("../output/trial_hist.png", width = 5, height = 3)Make a line-plot version of the histogram.
p_trial_hist_line <- ggplot(counts_by_day[course %in% c("English", "French")],
aes(x = doy_posix_aligned, y = trials_total_week, group = school_year, colour = school_year, fill = school_year)) +
facet_wrap(~ course, ncol = 1) +
geom_rect(xmin = date_schools_closed, xmax = date_schools_opened, ymin = -2e5, ymax = 2.2e6, fill = "grey92", colour = "grey50", lty = 2) +
geom_line() +
scale_x_datetime(expand = c(0, 0),
breaks = as.POSIXct(c(
"2019-09-01 02:00:00 CET",
"2019-11-01 02:00:00 CET",
"2020-01-01 02:00:00 CET",
"2020-03-01 02:00:00 CET",
"2020-05-01 02:00:00 CET",
"2020-07-01 02:00:00 CET")),
limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
date_labels = "%b") +
scale_y_continuous(expand = c(0, 0), limits = c(0, 2e6), labels = number_format) +
scale_colour_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
scale_fill_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
labs(x = NULL,
y = "Trials per week",
colour = "School year",
fill = "School year") +
theme_paper
p_trial_hist_lineWarning: Removed 101 row(s) containing missing values (geom_path).
ggsave("../output/trial_hist_line.pdf", width = 5, height = 3)Warning: Removed 101 row(s) containing missing values (geom_path).ggsave("../output/trial_hist_line.png", width = 5, height = 3)Warning: Removed 101 row(s) containing missing values (geom_path).Also make a difference plot.
# In order for the Mondays to align, move the 18/19 data forward by 1 year - 1 day.
counts_by_day[school_year == "18/19", doy_posix_aligned := as.POSIXct(doy_posix + 364*24*60*60, origin = "1970-01-01")]counts_by_day[school_year == "19/20", doy_posix_aligned := doy_posix]counts_by_day[, year_diff := trials_total_week[2] - trials_total_week[1], by = .(course, doy_posix_aligned)]ggplot(counts_by_day[course %in% c("English", "French")],
aes(x = doy_posix_aligned, y = year_diff)) +
facet_wrap(~ course, ncol = 1) +
geom_rect(xmin = date_schools_closed, xmax = date_schools_opened, ymin = -1e6, ymax = 1.1e6, fill = "grey92", colour = "grey50", lty = 2) +
geom_hline(yintercept = 0, lty = 3) +
geom_line() +
scale_x_datetime(expand = c(0, 0),
breaks = as.POSIXct(c(
"2019-09-01 02:00:00 CET",
"2019-11-01 02:00:00 CET",
"2020-01-01 02:00:00 CET",
"2020-03-01 02:00:00 CET",
"2020-05-01 02:00:00 CET",
"2020-07-01 02:00:00 CET")),
limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
date_labels = "%b") +
scale_y_continuous(expand = c(0, 0), limits = c(-3e5, 1e6), labels = number_format) +
labs(x = NULL,
y = "Trials per week",
colour = "School year",
fill = "School year") +
theme_paperWarning: Removed 101 row(s) containing missing values (geom_path).
Number of trials by user and week
counts[, doy_posix_week := cut.POSIXt(doy_posix, "week")]counts_by_user_and_week <- counts[, .(trials_user = sum(trials, na.rm = TRUE)), by = .(course, school_year, user, doy_posix_week)]Save for clustering analysis
saveRDS(na.omit(counts_by_user_and_week[course %in% c("English", "French")]), "../data/trials_by_user_and_week.rds")Unique users by day
users_by_day <- counts[, .(unique_users = length(unique(user))), by = .(course, doy_posix)]p <- ggplot(users_by_day, aes(x = doy_posix, y = unique_users, colour = course)) +
geom_line() +
scale_x_datetime(date_breaks = "3 months", date_labels = "%e %b %Y") +
scale_y_continuous(labels = number_format) +
labs(x = NULL,
y = "Number of users per day",
colour = "Course") +
theme_paper
p
Unique users by week
Use cut.Date() to bin dates by week. Each day is assigned the date of the most recent Monday.
users_by_day[, doy_posix_week := cut.POSIXt(doy_posix, "week")]users_by_week <- counts[, .(unique_users_week = length(unique(user))), by = .(course, doy_posix_week)]
users_by_week <- users_by_day[users_by_week, on = .(course, doy_posix_week)]p <- ggplot(users_by_week, aes(x = doy_posix, ymin = 0, ymax = unique_users_week, group = course, colour = course, fill = course)) +
facet_wrap(~ course, ncol = 1, scales = "free_y") +
geom_ribbon(alpha = .2) +
scale_x_datetime(date_breaks = "3 months", date_labels = "%e %b %Y") +
scale_y_continuous(labels = scales::number_format(big.mark = ".", decimal.mark = ",")) +
labs(x = NULL,
y = "Aantal gebruikers",
title = "Aantal verschillende gebruikers per week",
caption = "Let op: schaal verschilt tussen de grafieken",
colour = "Lesmethode",
fill = "Lesmethode") +
guides(colour = FALSE, fill = FALSE) +
theme_paper
p
Overlap the two school years:
users_by_week[, school_year := ifelse(doy_posix < "2019-08-01", "18/19", "19/20")]users_by_week[school_year == "18/19", doy_posix_aligned := as.POSIXct(doy_posix + 365*24*60*60, origin = "1970-01-01")]users_by_week[school_year == "19/20", doy_posix_aligned := doy_posix]p_user_hist <- ggplot(users_by_week[course %in% c("English", "French"),],
aes(x = doy_posix_aligned, ymin = 0, ymax = unique_users_week, group = school_year, colour = school_year, fill = school_year)) +
facet_wrap(~ course, ncol = 1) +
geom_rect(xmin = date_schools_closed, xmax = date_schools_opened, ymin = -800, ymax = 8800, fill = "grey92", colour = "grey50", lty = 2) +
geom_ribbon(alpha = .2) +
scale_x_datetime(expand = c(0, 0),
breaks = as.POSIXct(c(
"2019-09-01 02:00:00 CET",
"2019-11-01 02:00:00 CET",
"2020-01-01 02:00:00 CET",
"2020-03-01 02:00:00 CET",
"2020-05-01 02:00:00 CET",
"2020-07-01 02:00:00 CET")),
limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
date_labels = "%b") +
scale_y_continuous(expand = c(0, 0), limits = c(0, 8000), labels = number_format) +
scale_colour_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
scale_fill_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
labs(x = NULL,
y = "Unique users per week",
colour = "School year",
fill = "School year") +
theme_paper
p_user_hist
ggsave("../output/user_hist.pdf", width = 5, height = 3)
ggsave("../output/user_hist.png", width = 5, height = 3)Make a combined plot of trial and user counts for in the paper:
p_legend <- get_legend(p_trial_hist)
p_trial_hist <- p_trial_hist +
guides(colour = FALSE, fill = FALSE)
p_user_hist <- p_user_hist +
guides(colour = FALSE, fill = FALSE)plot_grid(plot_grid(p_trial_hist, p_user_hist,
labels = c("A", "B"),
align = "v", axis = "tblr"),
p_legend,
rel_widths = c(1, .2))
ggsave("../output/combi_hist.pdf", width = 9, height = 3)
ggsave("../output/combi_hist.png", width = 9, height = 3)Activity during the week
Get number of trials by method, day, hour, and user:
db <- db_connect()
counts_by_hour <- dbGetQuery(db,"SELECT responses.method AS 'method',
DATE(responses.date + 3600, 'unixepoch') AS 'doy',
STRFTIME('%H', responses.date + 3600, 'unixepoch') AS 'hour',
responses.user_id AS 'user',
COUNT(*) AS 'trials'
FROM 'responses'
GROUP BY responses.method,
DATE(responses.date + 3600, 'unixepoch'),
STRFTIME('%H', responses.date + 3600, 'unixepoch'),
responses.user_id
")
db_disconnect(db)
setDT(counts_by_hour)Interpolate missing days and hours:
counts_by_hour[, doy_posix := as.POSIXct(doy)]counts_by_hour[, hour := as.numeric(hour)]doy_posix <- seq.POSIXt(from = counts_by_hour[,min(doy_posix)], to = counts_by_hour[,max(doy_posix)], by = "DSTday")
counts_by_hourmethod <- counts_by_hour[,unique(method)]
hour <- 0:23
dates_and_hours <- CJ(doy_posix, hour, method)
counts_by_hour <- merge(counts_by_hour, dates_and_hours, by = c("doy_posix", "hour", "method"), all = TRUE)Add day of the week:
counts_by_hour[, weekday := weekdays(doy_posix)]Distinguish between school years:
counts_by_hour[, school_year := ifelse(doy_posix < "2019-08-01", "18/19", "19/20")]Add quarter:
counts_by_hour[, quarter := paste0(year(doy_posix), "Q", quarter(doy_posix))]Add exact school closure period in both school years:
counts_by_hour[school_year == "18/19", doy_posix_aligned := as.POSIXct(doy_posix + 365*24*60*60, origin = "1970-01-01")]counts_by_hour[school_year == "19/20", doy_posix_aligned := doy_posix]counts_by_hour[, schools_closed := doy_posix_aligned >= date_schools_closed & doy_posix < date_schools_opened]Add more sensible course names:
counts_by_hour[, course := ifelse(method == "Grandes Lignes", "French", ifelse(method == "Stepping Stones", "English", "German"))]Sum trials by school year, weekday and hour:
counts_by_hour[, trials_schoolyear := sum(trials, na.rm = TRUE), by = .(course, school_year, weekday, hour)]Also sum trials by quarter, weekday and hour:
counts_by_hour[, trials_quarter := sum(trials, na.rm = TRUE), by = .(course, quarter, weekday, hour)]And sum trials within the closure period by weekday and hour:
counts_by_hour[schools_closed == TRUE, trials_closed := sum(trials, na.rm = TRUE), by = .(course, school_year, weekday, hour)]trials_by_wday_hour <- unique(counts_by_hour, by = c("course", "school_year", "quarter", "schools_closed", "weekday", "hour"))
trials_by_wday_hour[, trials_normalised_schoolyear := trials_schoolyear / sum(trials_schoolyear), by = .(course)]trials_by_wday_hour[, trials_normalised_quarter := trials_quarter / sum(trials_quarter), by = .(course)]trials_by_wday_hour[, weekday := ordered(weekday, levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))]# trials_by_wday_hour[, weekday := ordered(weekday, levels = c("maandag", "dinsdag", "woensdag", "donderdag", "vrijdag", "zaterdag", "zondag"))]Plot heatmap for the whole school year:
ggplot(trials_by_wday_hour[course %in% c("English", "French")],
aes(x = hour, y = reorder(weekday, dplyr::desc(weekday)), fill = trials_normalised_schoolyear)) +
facet_grid(school_year ~ course) +
geom_tile(colour = "white", size = 0.25) +
labs(x = "Time of day (hour)",
y = NULL) +
scale_x_continuous(expand = c(0,0), breaks = seq(0, 24, 3)) +
scale_y_discrete(expand = c(0,0)) +
scale_fill_viridis_c(option = "A", direction = -1) +
coord_fixed() +
guides(fill = FALSE) +
theme_paper
Plot heatmap per quarter:
ggplot(trials_by_wday_hour, aes(x = hour, y = reorder(weekday, dplyr::desc(weekday)), fill = trials_normalised_quarter)) +
facet_grid(quarter ~ method) +
geom_tile(colour = "white", size = 0.25) +
labs(x = NULL,
y = NULL,
title = "Activiteit per uur gedurende de week",
caption = "Aantal trials per weekdag en uur in elk kwartaal, genormaliseerd per methode.") +
scale_x_continuous(expand = c(0,0), breaks = seq(0, 24, 3)) +
scale_y_discrete(expand = c(0,0)) +
scale_fill_viridis_c(option = "A", direction = -1) +
coord_fixed() +
guides(fill = FALSE) +
theme_bw(base_size = 16)
Plot heatmap for the period in which schools were closed:
trials_closed <- unique(trials_by_wday_hour[schools_closed == TRUE, .(course, school_year, weekday, hour, trials_closed)])
trials_closed[, trials_normalised_closed := trials_closed / sum(trials_closed), by = .(course, school_year)]trials_closed_diff <- trials_closed[, .(school_year = "Change",
trials_closed = trials_closed[school_year == "19/20"] - trials_closed[school_year == "18/19"],
trials_normalised_closed = trials_normalised_closed[school_year == "19/20"] - trials_normalised_closed[school_year == "18/19"]), by = .(course, weekday, hour)]p_heatmap <- ggplot(trials_closed[course %in% c("English", "French"),],
aes(x = hour, y = reorder(weekday, dplyr::desc(weekday)), fill = trials_normalised_closed)) +
facet_grid(school_year ~ course) +
geom_tile(colour = "white", size = 0.25) +
labs(x = "Time of day (hour)",
y = NULL,
fill = NULL) +
scale_x_continuous(expand = c(0,0), breaks = seq(0, 24, 3)) +
scale_y_discrete(expand = c(0,0)) +
scale_fill_viridis_c(option = "A", direction = -1) +
coord_fixed() +
theme_paper
p_heatmap
Make a plot of the difference between the two school years during the school closure period:
p_heatmap_diff <- ggplot(trials_closed_diff[course %in% c("English", "French"),],
aes(x = hour, y = reorder(weekday, dplyr::desc(weekday)), fill = trials_normalised_closed)) +
facet_grid(school_year ~ course) +
geom_tile(colour = "white", size = 0.25) +
labs(x = "Time of day (hour)",
y = NULL,
fill = NULL) +
scale_x_continuous(expand = c(0,0), breaks = seq(0, 24, 3)) +
scale_y_discrete(expand = c(0,0)) +
scale_fill_distiller(type = "div", palette = "RdBu", direction = -1, limits = c(-1, 1) * max(abs(trials_closed_diff[course %in% c("English", "French"),]$trials_normalised_closed))) +
coord_fixed() +
theme_paper
p_heatmap_diff
Make a combined plot for in the paper:
p_heatmap_legend <- get_legend(p_heatmap)
p_heatmap_diff_legend <- get_legend(p_heatmap_diff)
p_heatmap <- p_heatmap + guides(fill = FALSE)
p_heatmap_diff <- p_heatmap_diff + guides(fill = FALSE)plot_grid(
plot_grid(p_heatmap, p_heatmap_diff,
ncol = 1,
labels = c("A", "B"),
rel_heights = c(1, .655)
),
plot_grid(p_heatmap_legend, p_heatmap_diff_legend,
ncol = 1,
align = "vh", axis = "lrtb"),
ncol = 2,
rel_widths = c(1, .15))
ggsave("../output/combi_heatmap.pdf", width = 9, height = 5)
ggsave("../output/combi_heatmap.png", width = 9, height = 5)Activity stratified by year and level
db <- db_connect()
counts_strat <- dbGetQuery(db,"SELECT responses.method AS 'method',
responses.book_info_id as 'book_info_id',
DATE(responses.date + 3600, 'unixepoch') AS 'doy',
responses.user_id AS 'user',
COUNT(*) AS 'trials'
FROM 'responses'
GROUP BY responses.method, responses.book_info_id,
DATE(responses.date + 3600, 'unixepoch'),
responses.user_id
")
db_disconnect(db)
setDT(counts_strat)db <- db_connect()
book_info <- dbGetQuery(db, "SELECT * FROM 'book_info'")
db_disconnect(db)
setDT(book_info)Add book information:
counts_strat[book_info, on = "book_info_id", c("book_title", "method_group") := .(i.book_title, i.method_group)]Add a school year column (cutoff date: 1 August):
counts_strat[, doy_posix := as.POSIXct(doy)]counts_strat[, school_year := ifelse(doy_posix < "2019-08-01", "18/19", "19/20")]Add sensible course names:
counts_strat[, course := ifelse(method == "Grandes Lignes", "French", ifelse(method == "Stepping Stones", "English", "German"))]Count trials by day:
counts_strat_by_day <- counts_strat[, .(trials_total = sum(trials, na.rm = TRUE)), by = .(school_year, course, method_group, book_title, doy_posix)]
setorder(counts_strat_by_day, school_year, course, method_group, book_title, doy_posix)Simplify level names:
# Keep all distinctions
counts_strat_by_day[, book_title_simple := stringr::str_sub(book_title, 3, -10)]counts_strat_by_day[, book_title_simple := factor(book_title_simple, levels = c("vmbo b/lwoo", "vmbo b", "vmbo bk", "vmbo k", "vmbo kgt", "vmbo-gt", "vmbo gt", "vmbo-gt/havo", "vmbo (t)hv", "havo", "havo vwo", "vwo"))]# Simplify to three levels
counts_strat_by_day[, level := dplyr::case_when(
grepl("vmbo", book_title) ~ "Pre-vocational\n(vmbo)",
grepl("havo", book_title) ~ "General secondary\n(havo)",
grepl("vwo", book_title) ~ "Pre-university\n(vwo)",
TRUE ~ "Other")]counts_strat_by_day[, level := factor(level, levels = c("Other", "Pre-vocational\n(vmbo)", "General secondary\n(havo)", "Pre-university\n(vwo)"))]Simplify year names:
counts_strat_by_day[, year := dplyr::case_when(
method_group == "Leerjaar 1 (5e Ed.)" ~ "Year 1",
method_group == "Leerjaar 2 (5e Ed.)" ~ "Year 2",
method_group == "Leerjaar 3 (5e Ed.)" ~ "Year 3",
method_group == "Leerjaar 3/4 (5e Ed.)" ~ "Year 3/4",
method_group == "Leerjaar 4 (5e Ed.)" ~ "Year 4",
method_group == "Tweede Fase (6e Ed.)" ~ "Tweede Fase",
TRUE ~ "Other")]Align school years:
counts_strat_by_day[school_year == "18/19", doy_posix_aligned := as.POSIXct(doy_posix + 365*24*60*60, origin = "1970-01-01")]counts_strat_by_day[school_year == "19/20", doy_posix_aligned := doy_posix]Use cut.Date() to bin dates by week. Each day is assigned the date of the most recent Monday.
counts_strat_by_day[, doy_posix_aligned_week := cut.POSIXt(doy_posix_aligned, "week")]counts_strat_by_day[, trials_total_week := sum(trials_total, na.rm = TRUE), by = .(school_year, course, method_group, book_title_simple, doy_posix_aligned_week)]counts_strat_by_day[, trials_total_week_level := sum(trials_total), by = .(school_year, course, method_group, level, doy_posix_aligned_week)]Summarise increase during lockdown:
counts_strat_increase <- counts_strat_by_day[between(doy_posix_aligned, date_schools_closed, date_schools_opened), .(trials_lockdown = sum(trials_total)), by = .(course, book_title_simple, method_group, year, school_year)]
counts_strat_increase[, increase := trials_lockdown[2]/trials_lockdown[1], by = .(course, book_title_simple, method_group, year)]counts_strat_increase[, increase_pct := paste0("Change:\n", scales::percent(increase, accuracy = 2))]counts_strat_increase_level <- counts_strat_by_day[between(doy_posix_aligned, date_schools_closed, date_schools_opened), .(trials_lockdown = sum(trials_total)), by = .(course, level, method_group, year, school_year)]
counts_strat_increase_level[, increase := trials_lockdown[2]/trials_lockdown[1], by = .(course, level, method_group, year)]counts_strat_increase_level[, increase_pct := paste0("Change:\n", scales::percent(increase, accuracy = 2))]French
ggplot(counts_strat_by_day[course == "French"],
aes(group = school_year, colour = school_year, fill = school_year)) +
facet_grid(book_title_simple ~ method_group) +
geom_rect(xmin = date_schools_closed, xmax = date_schools_opened, ymin = -2e5, ymax = 2.2e6, fill = "grey92", colour = "grey50", lty = 2) +
geom_ribbon(aes(x = doy_posix_aligned, ymin = 0, ymax = trials_total_week, ), alpha = .2) +
geom_text(data = counts_strat_increase[course == "French" & school_year == "19/20"],
aes(label = increase_pct),
x = as.POSIXct((as.numeric(date_schools_closed) + as.numeric(date_schools_opened))/2, origin = "1970-01-01"),
y = 3.6e5,
colour = "black",
vjust = 1,
show.legend = FALSE) +
scale_x_datetime(expand = c(0, 0),
breaks = as.POSIXct(c(
"2019-09-01 02:00:00 CET",
"2019-11-01 02:00:00 CET",
"2020-01-01 02:00:00 CET",
"2020-03-01 02:00:00 CET",
"2020-05-01 02:00:00 CET",
"2020-07-01 02:00:00 CET")),
limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
date_labels = "%b") +
scale_y_continuous(expand = c(0, 0), limits = c(0, 3.75e5), labels = number_format) +
scale_colour_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
scale_fill_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
labs(x = NULL,
y = "Trials per week",
colour = "School year",
fill = "School year",
title = "French") +
theme_paper
ggsave("../output/trial_hist_french.pdf", width = 14, height = 10)
ggsave("../output/trial_hist_french.png", width = 14, height = 10)Streamlined version for in the paper:
ggplot(counts_strat_by_day[course == "French"],
aes(group = school_year, colour = school_year, fill = school_year)) +
facet_grid(level ~ year) +
geom_rect(xmin = date_schools_closed, xmax = date_schools_opened, ymin = -2e5, ymax = 2.2e6, fill = "grey92", colour = "grey50", lty = 2) +
geom_ribbon(aes(x = doy_posix_aligned, ymin = 0, ymax = trials_total_week_level, ), alpha = .2) +
geom_text(data = counts_strat_increase_level[course == "French" & school_year == "19/20"],
aes(label = increase_pct),
x = as.POSIXct((as.numeric(date_schools_closed) + as.numeric(date_schools_opened))/2, origin = "1970-01-01"),
y = 3.6e5,
colour = "black",
vjust = 1,
size = rel(2.75),
show.legend = FALSE) +
scale_x_datetime(expand = c(0, 0),
breaks = as.POSIXct(c(
"2019-10-01 02:00:00 CET",
"2019-12-01 02:00:00 CET",
"2020-02-01 02:00:00 CET",
"2020-04-01 02:00:00 CET",
"2020-06-01 02:00:00 CET")),
limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
date_labels = "%b") +
scale_y_continuous(expand = c(0, 0), limits = c(0, 3.75e5), labels = number_format) +
scale_colour_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
scale_fill_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
labs(x = NULL,
y = "Trials per week",
colour = "School year",
fill = "School year") +
theme_paper
ggsave("../output/trial_hist_french_level.pdf", width = 9, height = 5)
ggsave("../output/trial_hist_french_level.png", width = 9, height = 5)English
ggplot(counts_strat_by_day[course == "English"],
aes(group = school_year, colour = school_year, fill = school_year)) +
facet_grid(book_title_simple ~ method_group) +
geom_rect(xmin = date_schools_closed, xmax = date_schools_opened, ymin = -2e5, ymax = 2.2e6, fill = "grey92", colour = "grey50", lty = 2) +
geom_ribbon(aes(x = doy_posix_aligned, ymin = 0, ymax = trials_total_week, ), alpha = .2) +
geom_text(data = counts_strat_increase[course == "English" & school_year == "19/20"],
aes(label = increase_pct),
x = as.POSIXct((as.numeric(date_schools_closed) + as.numeric(date_schools_opened))/2, origin = "1970-01-01"),
y = 3.6e5,
colour = "black",
vjust = 1,
show.legend = FALSE) +
scale_x_datetime(expand = c(0, 0),
breaks = as.POSIXct(c(
"2019-09-01 02:00:00 CET",
"2019-11-01 02:00:00 CET",
"2020-01-01 02:00:00 CET",
"2020-03-01 02:00:00 CET",
"2020-05-01 02:00:00 CET",
"2020-07-01 02:00:00 CET")),
limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
date_labels = "%b") +
scale_y_continuous(expand = c(0, 0), limits = c(0, 3.75e5), labels = number_format) +
scale_colour_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
scale_fill_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
labs(x = NULL,
y = "Trials per week",
colour = "School year",
fill = "School year",
title = "English") +
theme_paper
ggsave("../output/trial_hist_english.pdf", width = 14, height = 10)
ggsave("../output/trial_hist_english.png", width = 14, height = 10)Streamlined version for in the paper:
ggplot(counts_strat_by_day[course == "English" & level != "Other"],
aes(group = school_year, colour = school_year, fill = school_year)) +
facet_grid(level ~ year) +
geom_rect(xmin = date_schools_closed, xmax = date_schools_opened, ymin = -2e5, ymax = 2.2e6, fill = "grey92", colour = "grey50", lty = 2) +
geom_ribbon(aes(x = doy_posix_aligned, ymin = 0, ymax = trials_total_week_level, ), alpha = .2) +
geom_text(data = counts_strat_increase_level[course == "English" & level != "Other" & school_year == "19/20"],
aes(label = increase_pct),
x = as.POSIXct((as.numeric(date_schools_closed) + as.numeric(date_schools_opened))/2, origin = "1970-01-01"),
y = 9.6e5,
colour = "black",
vjust = 1,
size = rel(2.75),
show.legend = FALSE) +
scale_x_datetime(expand = c(0, 0),
breaks = as.POSIXct(c(
"2019-10-01 02:00:00 CET",
"2019-12-01 02:00:00 CET",
"2020-02-01 02:00:00 CET",
"2020-04-01 02:00:00 CET",
"2020-06-01 02:00:00 CET")),
limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
date_labels = "%b") +
scale_y_continuous(expand = c(0, 0), limits = c(0, 1e6), labels = number_format) +
scale_colour_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
scale_fill_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
labs(x = NULL,
y = "Trials per week",
colour = "School year",
fill = "School year") +
theme_paper
ggsave("../output/trial_hist_english_level.pdf", width = 9, height = 5)
ggsave("../output/trial_hist_english_level.png", width = 9, height = 5)Question type
There are different question formats: open-answer, in which the student types the answer, and multiple-choice, in which the student selects the answer from a set of 3 or 4 options.
db <- db_connect()
question_type <- dbGetQuery(db,
"SELECT r.method AS 'method',
DATE(r.date + 3600, 'unixepoch') AS 'doy',
r.choices AS 'choices',
COUNT(*) AS 'n'
FROM 'responses' r
WHERE r.study == 0
GROUP BY r.method,
DATE(r.date + 3600, 'unixepoch'),
r.choices"
)
setDT(question_type)
db_disconnect(db)Add a school year column (cutoff date: 1 August):
question_type[, doy_posix := as.POSIXct(doy)]question_type[, school_year := ifelse(doy_posix < "2019-08-01", "18/19", "19/20")]Add sensible course names:
question_type[, course := ifelse(method == "Grandes Lignes", "French", ifelse(method == "Stepping Stones", "English", "German"))]Align school years:
question_type[school_year == "18/19", doy_posix_aligned := as.POSIXct(doy_posix + 365*24*60*60, origin = "1970-01-01")]question_type[school_year == "19/20", doy_posix_aligned := doy_posix]Use cut.Date() to bin dates by week. Each day is assigned the date of the most recent Monday.
question_type[, doy_posix_week := cut.POSIXt(doy_posix, "week")]question_type[, doy_posix_aligned_week := cut.POSIXt(doy_posix_aligned, "week")]question_type_by_week <- question_type[, .(n = sum(n)), by = .(course, school_year, doy_posix_aligned_week, choices)]ggplot(question_type_by_week[course %in% c("English", "French")], aes(x = as.POSIXct(doy_posix_aligned_week), y = n, group = interaction(school_year,as.factor(choices)), colour = school_year)) +
facet_grid(course ~ choices) +
geom_line() +
scale_x_datetime(expand = c(0, 0),
breaks = as.POSIXct(c(
"2019-10-01 02:00:00 CET",
"2019-12-01 02:00:00 CET",
"2020-02-01 02:00:00 CET",
"2020-04-01 02:00:00 CET",
"2020-06-01 02:00:00 CET")),
limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
date_labels = "%b") +
labs(x = NULL,
y = "Trials",
colour = "School year") +
theme_paperWarning: Removed 24 row(s) containing missing values (geom_path).
question_type[, .(n = sum(n)), by = .(course, mcq = choices>1, school_year)][, .(perc_mcq = n[mcq == TRUE]/sum(n)), by = .(course, school_year)]There is a clear difference between the languages in the question format used: English uses almost exclusively 4-alternative MCQs, while French uses a mix of MCQs (including a small number of 3-alternative questions) and open-answer questions.
Session info
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=nl_NL.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=nl_NL.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=nl_NL.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] cowplot_0.9.4 ggplot2_3.3.2 DBI_1.1.0 data.table_1.12.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.2 RColorBrewer_1.1-2 pillar_1.4.2
[4] compiler_3.6.3 base64enc_0.1-3 tools_3.6.3
[7] digest_0.6.19 bit_1.1-14 viridisLite_0.3.0
[10] memoise_1.1.0 jsonlite_1.6 evaluate_0.14
[13] RSQLite_2.2.0 tibble_2.1.3 gtable_0.3.0
[16] pkgconfig_2.0.2 rlang_0.4.4 yaml_2.2.0
[19] xfun_0.7 withr_2.3.0 stringr_1.4.0
[22] dplyr_0.8.3 knitr_1.23 vctrs_0.2.2
[25] bit64_0.9-7 tidyselect_0.2.5 glue_1.3.1
[28] R6_2.4.0 rmarkdown_1.13 blob_1.2.1
[31] purrr_0.3.2 magrittr_1.5 scales_1.0.0
[34] htmltools_0.3.6 assertthat_0.2.1 colorspace_1.4-1
[37] labeling_0.3 stringi_1.4.3 munsell_0.5.0
[40] crayon_1.3.4 ---
title: 'SlimStampen Usage During Lockdown'
author: "Maarten van der Velde"
date: "Last updated: `r Sys.Date()`"
output:
  html_notebook:
    smart: no
    toc: yes
    toc_float: yes
  html_document:
    df_print: paged
    toc: no
    toc_float: yes
editor_options: 
  chunk_output_type: inline
---

# Setup

```{r}
library(data.table)
library(DBI)
library(ggplot2)
library(cowplot)
library(grid)

Sys.setlocale("LC_TIME", "en_US.UTF-8") # Print English date format
# Sys.setlocale("LC_TIME", "nl_NL.UTF-8") # Print Dutch date format

number_format <- scales::number_format(big.mark = ",", decimal.mark = ".") # Print English number format
# number_format <- scales::number_format(big.mark = ".", decimal.mark = ",") # Print Dutch number format

theme_paper <- theme_classic(base_size = 12) + 
  theme(axis.text = element_text(colour = "black"),
        panel.grid.major.y = element_line(colour = "grey92"))
```

School closure and opening dates

Sources:
- https://www.rijksoverheid.nl/actueel/nieuws/2020/03/15/aanvullende-maatregelen-onderwijs-horeca-sport
- https://www.rijksoverheid.nl/actueel/nieuws/2020/05/19/onderwijs-gaat-stap-voor-stap-open
```{r}
date_schools_closed <- as.POSIXct("2020-03-16")
date_schools_opened <- as.POSIXct("2020-06-02")
```


Handle database connections
```{r}
db_connect <- function() {
  db <- dbConnect(RSQLite::SQLite(), file.path("..", "data", "noordhoff.sqlite"))
  return(db)
}

db_disconnect <- function(db) {
  dbDisconnect(db)
}
```


# Data

The database contains all SlimStampen data collected via Noordhoff's platform in three courses: *Stepping Stones* (English), *Grandes Lignes* (French), and *Neue Kontakte* (German).

Trial-level response data are stored in the `responses` table.
Book information, such as the course year, book title, and chapter, are stored in the `book_info` table.

## `responses`

| Column               | Type      | Explanation                                   |
|----------------------|-----------|-----------------------------------------------|
| date                 | int       | UNIX time stamp [s]                           |
| user_id              | chr       | unique user identifier                        |
| method               | chr       | course                                        |
| start_time           | int       | elapsed time since session start [ms]         |
| rt                   | int       | response time [ms]                            |
| duration             | int       | trial duration [ms]                           |
| fact_id              | int       | unique fact identifier (within chapter)       |
| correct              | int       | response accuracy                             |
| answer               | chr       | user's response                               |
| choices              | int       | number of answer choices (1 == open response) |
| backspace_used       | dbl       | user pressed backspace during trial           |
| backspace_used_first | dbl       | user erased first character of response       |
| study                | int       | trial was a study trial                       |
| answer_language      | chr       | language of the answer                        |
| subsession           | int       | identifies part within learning session       |
| book_info_id         | chr       | unique identifier of book information         |


## `book_info`

| Column               | Type      | Explanation                                   |
|----------------------|-----------|-----------------------------------------------|
| book_info_id         | chr       | unique identifier of book information         |
| method_group         | chr       | year and edition                              |
| book_title           | chr       | book title (incl. year, level, edition)       |
| book_type            | chr       | type of book                                  |
| chapter              | chr       | chapter number and title                      |


Preview first 10 rows
```{r}
db <- db_connect()
responses_top <- dbGetQuery(db, "SELECT * FROM responses LIMIT 10")
responses_top

book_info_top <- dbGetQuery(db, "SELECT * FROM book_info LIMIT 10")
book_info_top
db_disconnect(db)
```



# Usage

Get number of trials by method, day, and user:
```{r}
db <- db_connect()
counts <- dbGetQuery(db,"SELECT responses.method AS 'method',
                          DATE(responses.date + 3600, 'unixepoch') AS 'doy',
                          responses.user_id AS 'user',
                          COUNT(*) AS 'trials'
                          FROM 'responses'
                          GROUP BY responses.method,
                          DATE(responses.date  + 3600, 'unixepoch'),
                          responses.user_id
                        ")
db_disconnect(db)

setDT(counts)
```

Add a school year column (cutoff date: 1 August):
```{r}
counts[, doy_posix := as.POSIXct(doy)]
counts[, school_year := ifelse(doy_posix < "2019-08-01", "18/19", "19/20")]
```

Add more sensible course names:
```{r}
counts[, course := ifelse(method == "Grandes Lignes", "French", ifelse(method == "Stepping Stones", "English", "German"))]
```


## Total number of unique users by course
```{r}
counts[, .(unique_users = length(unique(user))), by = .(course, school_year)]
```


## Total number of trials by course
```{r}
counts[, .(total_trials = sum(trials)), by = .(course, school_year)]
```

## Number of trials by user

```{r}
counts[, trials_user := sum(trials), by = .(course, user)]
ggplot(counts, aes(x = trials_user)) +
  facet_wrap(~course, ncol = 1, scales = "free_y") +
  geom_histogram(binwidth = 100) +
  labs(x = "Number of trials by user",
       y = NULL) +
  theme_paper

```

## Number of unique days by user
```{r}
ggplot(counts[, .(N = length(unique(doy_posix))), by = c("user", "course")], aes(x = N)) +
  facet_grid(course ~ ., scales = "free_y") +
  geom_histogram(binwidth = 1) +
  geom_vline(xintercept = (1:7)*7, lty = 2, alpha = 0.5) +
  labs(x = "Number of unique days by user",
       y = NULL) +
  theme_paper
```


## Total number of trials by day

Interpolate missing days:
```{r}
doy_posix <- seq.POSIXt(from = counts[,min(doy_posix)], to = counts[,max(doy_posix)], by = "DSTday")
course <- counts[,unique(course)]
dates <- CJ(doy_posix, course)
counts <- merge(counts, dates, by = c("doy_posix", "course"), all = TRUE)
```

Count trials by day:
```{r}
counts[, trials_total := sum(trials, na.rm = TRUE), by = .(course, doy_posix)]
```

```{r}
counts_by_day <- counts[, .(trials_total = sum(trials, na.rm = TRUE)), by = .(course, doy_posix)]
```


```{r}
ggplot(counts_by_day[course %in% c("English", "French"),],
       aes(x = doy_posix, y = trials_total, colour = course)) +
  geom_line() +
  scale_x_datetime(date_breaks = "3 months", date_labels = "%e %b %Y") +
  scale_y_continuous(labels = number_format) +
  labs(x = NULL,
       y = "Number of trials per day",
       colour = "Course") +
  theme_paper
```


## Total number of trials by week

Use cut.Date() to bin dates by week. Each day is assigned the date of the most recent Monday.
```{r}
counts_by_day[, doy_posix_week := cut.POSIXt(doy_posix, "week")]
counts_by_day[, trials_total_week := sum(trials_total, na.rm = TRUE), by = .(course, doy_posix_week)]
```


```{r}
ggplot(counts_by_day[course %in% c("English", "French"),],
            aes(x = doy_posix, y = trials_total_week, colour = course)) +
  geom_line() +
  scale_x_datetime(date_breaks = "3 months", date_labels = "%e %b %Y") +
  scale_y_continuous(labels = number_format) +
  labs(x = NULL,
       y = "Number of trials per week",
       colour = "Course") +
  theme_paper
```


Overlap the two school years:
```{r}
counts_by_day[, school_year := ifelse(doy_posix < "2019-08-01", "18/19", "19/20")]
counts_by_day[school_year == "18/19", doy_posix_aligned := as.POSIXct(doy_posix + 365*24*60*60, origin = "1970-01-01")]
counts_by_day[school_year == "19/20", doy_posix_aligned := doy_posix]
```

```{r}
p_trial_hist <- ggplot(counts_by_day[course %in% c("English", "French"),],
            aes(x = doy_posix_aligned, ymin = 0, ymax = trials_total_week, group = school_year, colour = school_year, fill = school_year)) +
  facet_wrap(~ course, ncol = 1) +
  geom_rect(xmin = date_schools_closed, xmax = date_schools_opened, ymin = -2e5, ymax = 2.2e6, fill = "grey92", colour = "grey50", lty = 2) +
  geom_ribbon(alpha = .2) +
  scale_x_datetime(expand = c(0, 0), 
                   breaks = as.POSIXct(c(
                     "2019-09-01 02:00:00 CET",
                     "2019-11-01 02:00:00 CET",
                     "2020-01-01 02:00:00 CET",
                     "2020-03-01 02:00:00 CET",
                     "2020-05-01 02:00:00 CET",
                     "2020-07-01 02:00:00 CET")),
                   limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
                   date_labels = "%b") +
  scale_y_continuous(expand = c(0, 0), limits = c(0, 2e6), labels = number_format) +
  scale_colour_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
  scale_fill_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
  labs(x = NULL,
       y = "Trials per week",
       colour = "School year",
       fill = "School year") +
  theme_paper

p_trial_hist

ggsave("../output/trial_hist.pdf", width = 5, height = 3)
ggsave("../output/trial_hist.png", width = 5, height = 3)
```

Make a line-plot version of the histogram.
```{r}
p_trial_hist_line <- ggplot(counts_by_day[course %in% c("English", "French")],
            aes(x = doy_posix_aligned, y = trials_total_week, group = school_year, colour = school_year, fill = school_year)) +
  facet_wrap(~ course, ncol = 1) +
  geom_rect(xmin = date_schools_closed, xmax = date_schools_opened, ymin = -2e5, ymax = 2.2e6, fill = "grey92", colour = "grey50", lty = 2) +
  geom_line() +
  scale_x_datetime(expand = c(0, 0), 
                   breaks = as.POSIXct(c(
                     "2019-09-01 02:00:00 CET",
                     "2019-11-01 02:00:00 CET",
                     "2020-01-01 02:00:00 CET",
                     "2020-03-01 02:00:00 CET",
                     "2020-05-01 02:00:00 CET",
                     "2020-07-01 02:00:00 CET")),
                   limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
                   date_labels = "%b") +
  scale_y_continuous(expand = c(0, 0), limits = c(0, 2e6), labels = number_format) +
  scale_colour_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
  scale_fill_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
  labs(x = NULL,
       y = "Trials per week",
       colour = "School year",
       fill = "School year") +
  theme_paper

p_trial_hist_line

ggsave("../output/trial_hist_line.pdf", width = 5, height = 3)
ggsave("../output/trial_hist_line.png", width = 5, height = 3)
```

Also make a difference plot.
```{r}
# In order for the Mondays to align, move the 18/19 data forward by 1 year - 1 day.
counts_by_day[school_year == "18/19", doy_posix_aligned := as.POSIXct(doy_posix + 364*24*60*60, origin = "1970-01-01")]
counts_by_day[school_year == "19/20", doy_posix_aligned := doy_posix]
```

```{r}
counts_by_day[, year_diff := trials_total_week[2] - trials_total_week[1], by = .(course, doy_posix_aligned)]

ggplot(counts_by_day[course %in% c("English", "French")],
            aes(x = doy_posix_aligned, y = year_diff)) +
  facet_wrap(~ course, ncol = 1) +
  geom_rect(xmin = date_schools_closed, xmax = date_schools_opened, ymin = -1e6, ymax = 1.1e6, fill = "grey92", colour = "grey50", lty = 2) +
  geom_hline(yintercept = 0, lty = 3) +
  geom_line() +
  scale_x_datetime(expand = c(0, 0), 
                   breaks = as.POSIXct(c(
                     "2019-09-01 02:00:00 CET",
                     "2019-11-01 02:00:00 CET",
                     "2020-01-01 02:00:00 CET",
                     "2020-03-01 02:00:00 CET",
                     "2020-05-01 02:00:00 CET",
                     "2020-07-01 02:00:00 CET")),
                   limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
                   date_labels = "%b") +
  scale_y_continuous(expand = c(0, 0), limits = c(-3e5, 1e6), labels = number_format) +
  labs(x = NULL,
       y = "Trials per week",
       colour = "School year",
       fill = "School year") +
  theme_paper
```


## Number of trials by user and week
```{r}
counts[, doy_posix_week := cut.POSIXt(doy_posix, "week")]
counts_by_user_and_week <- counts[, .(trials_user = sum(trials, na.rm = TRUE)), by = .(course, school_year, user, doy_posix_week)]
```

Save for clustering analysis
```{r}
saveRDS(na.omit(counts_by_user_and_week[course %in% c("English", "French")]), "../data/trials_by_user_and_week.rds")
```


## Unique users by day

```{r}
users_by_day <- counts[, .(unique_users = length(unique(user))), by = .(course, doy_posix)]
```

```{r}
p <- ggplot(users_by_day, aes(x = doy_posix, y = unique_users, colour = course)) +
  geom_line() +
  scale_x_datetime(date_breaks = "3 months", date_labels = "%e %b %Y") +
  scale_y_continuous(labels = number_format) +
  labs(x = NULL,
       y = "Number of users per day",
       colour = "Course") +
  theme_paper

p
```


## Unique users by week

Use cut.Date() to bin dates by week. Each day is assigned the date of the most recent Monday.
```{r}
users_by_day[, doy_posix_week := cut.POSIXt(doy_posix, "week")]
```

```{r}
users_by_week <- counts[, .(unique_users_week = length(unique(user))), by = .(course, doy_posix_week)]
users_by_week <- users_by_day[users_by_week, on = .(course, doy_posix_week)]
```

```{r}
p <- ggplot(users_by_week, aes(x = doy_posix, ymin = 0, ymax = unique_users_week, group = course, colour = course, fill = course)) +
  facet_wrap(~ course, ncol = 1, scales = "free_y") +
  geom_ribbon(alpha = .2) +
  scale_x_datetime(date_breaks = "3 months", date_labels = "%e %b %Y") +
  scale_y_continuous(labels = scales::number_format(big.mark = ".", decimal.mark = ",")) +
  labs(x = NULL,
       y = "Aantal gebruikers",
       title = "Aantal verschillende gebruikers per week",
       caption = "Let op: schaal verschilt tussen de grafieken",
       colour = "Lesmethode",
       fill = "Lesmethode") +
  guides(colour = FALSE, fill = FALSE) +
  theme_paper

p
```

Overlap the two school years:
```{r}
users_by_week[, school_year := ifelse(doy_posix < "2019-08-01", "18/19", "19/20")]
users_by_week[school_year == "18/19", doy_posix_aligned := as.POSIXct(doy_posix + 365*24*60*60, origin = "1970-01-01")]
users_by_week[school_year == "19/20", doy_posix_aligned := doy_posix]
```


```{r}
p_user_hist <- ggplot(users_by_week[course %in% c("English", "French"),],
            aes(x = doy_posix_aligned, ymin = 0, ymax = unique_users_week, group = school_year, colour = school_year, fill = school_year)) +
  facet_wrap(~ course, ncol = 1) +
  geom_rect(xmin = date_schools_closed, xmax = date_schools_opened, ymin = -800, ymax = 8800, fill = "grey92", colour = "grey50", lty = 2) +
  geom_ribbon(alpha = .2) +
  scale_x_datetime(expand = c(0, 0), 
                   breaks = as.POSIXct(c(
                     "2019-09-01 02:00:00 CET",
                     "2019-11-01 02:00:00 CET",
                     "2020-01-01 02:00:00 CET",
                     "2020-03-01 02:00:00 CET",
                     "2020-05-01 02:00:00 CET",
                     "2020-07-01 02:00:00 CET")),
                   limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
                   date_labels = "%b") +
  scale_y_continuous(expand = c(0, 0), limits = c(0, 8000), labels = number_format) +
  scale_colour_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
  scale_fill_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
  labs(x = NULL,
       y = "Unique users per week",
       colour = "School year",
       fill = "School year") +
  theme_paper

p_user_hist

ggsave("../output/user_hist.pdf", width = 5, height = 3)
ggsave("../output/user_hist.png", width = 5, height = 3)
```


Make a combined plot of trial and user counts for in the paper:
```{r}
p_legend <- get_legend(p_trial_hist)

p_trial_hist <- p_trial_hist +
  guides(colour = FALSE, fill = FALSE)

p_user_hist <- p_user_hist +
  guides(colour = FALSE, fill = FALSE)
```

```{r}
plot_grid(plot_grid(p_trial_hist, p_user_hist,
          labels = c("A", "B"),
          align = "v", axis = "tblr"),
          p_legend,
          rel_widths = c(1, .2))

ggsave("../output/combi_hist.pdf", width = 9, height = 3)
ggsave("../output/combi_hist.png", width = 9, height = 3)
```


## Activity during the week

Get number of trials by method, day, hour, and user:
```{r}
db <- db_connect()
counts_by_hour <- dbGetQuery(db,"SELECT responses.method AS 'method',
                          DATE(responses.date + 3600, 'unixepoch') AS 'doy',
                          STRFTIME('%H', responses.date + 3600, 'unixepoch') AS 'hour',
                          responses.user_id AS 'user',
                          COUNT(*) AS 'trials'
                          FROM 'responses'
                          GROUP BY responses.method,
                          DATE(responses.date + 3600, 'unixepoch'),
                          STRFTIME('%H', responses.date + 3600, 'unixepoch'),
                          responses.user_id
                        ")
db_disconnect(db)

setDT(counts_by_hour)
```

Interpolate missing days and hours:
```{r}
counts_by_hour[, doy_posix := as.POSIXct(doy)]
counts_by_hour[, hour := as.numeric(hour)]
doy_posix <- seq.POSIXt(from = counts_by_hour[,min(doy_posix)], to = counts_by_hour[,max(doy_posix)], by = "DSTday")
counts_by_hour
method <- counts_by_hour[,unique(method)]
hour <- 0:23
dates_and_hours <- CJ(doy_posix, hour, method)
counts_by_hour <- merge(counts_by_hour, dates_and_hours, by = c("doy_posix", "hour", "method"), all = TRUE)
```

Add day of the week:
```{r}
counts_by_hour[, weekday := weekdays(doy_posix)]
```

Distinguish between school years:
```{r}
counts_by_hour[, school_year := ifelse(doy_posix < "2019-08-01", "18/19", "19/20")]
```

Add quarter:
```{r}
counts_by_hour[, quarter := paste0(year(doy_posix), "Q", quarter(doy_posix))]
```


Add exact school closure period in both school years:
```{r}
counts_by_hour[school_year == "18/19", doy_posix_aligned := as.POSIXct(doy_posix + 365*24*60*60, origin = "1970-01-01")]
counts_by_hour[school_year == "19/20", doy_posix_aligned := doy_posix]

counts_by_hour[, schools_closed := doy_posix_aligned >= date_schools_closed & doy_posix < date_schools_opened]
```


Add more sensible course names:
```{r}
counts_by_hour[, course := ifelse(method == "Grandes Lignes", "French", ifelse(method == "Stepping Stones", "English", "German"))]
```


Sum trials by school year, weekday and hour:
```{r}
counts_by_hour[, trials_schoolyear := sum(trials, na.rm = TRUE), by = .(course, school_year, weekday, hour)]
```

Also sum trials by quarter, weekday and hour:
```{r}
counts_by_hour[, trials_quarter := sum(trials, na.rm = TRUE), by = .(course, quarter, weekday, hour)]
```

And sum trials within the closure period by weekday and hour:
```{r}
counts_by_hour[schools_closed == TRUE, trials_closed := sum(trials, na.rm = TRUE), by = .(course, school_year, weekday, hour)]
```


```{r}
trials_by_wday_hour <- unique(counts_by_hour, by = c("course", "school_year", "quarter", "schools_closed", "weekday", "hour"))

trials_by_wday_hour[, trials_normalised_schoolyear := trials_schoolyear / sum(trials_schoolyear), by = .(course)]
trials_by_wday_hour[, trials_normalised_quarter := trials_quarter / sum(trials_quarter), by = .(course)]

trials_by_wday_hour[, weekday := ordered(weekday, levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))]
# trials_by_wday_hour[, weekday := ordered(weekday, levels = c("maandag", "dinsdag", "woensdag", "donderdag", "vrijdag", "zaterdag", "zondag"))]
```


Plot heatmap for the whole school year:
```{r}
ggplot(trials_by_wday_hour[course %in% c("English", "French")],
       aes(x = hour, y = reorder(weekday, dplyr::desc(weekday)), fill = trials_normalised_schoolyear)) + 
  facet_grid(school_year ~ course) +
  geom_tile(colour = "white", size = 0.25) +
  labs(x = "Time of day (hour)",
       y = NULL) +
  scale_x_continuous(expand = c(0,0), breaks = seq(0, 24, 3)) +
  scale_y_discrete(expand = c(0,0)) + 
  scale_fill_viridis_c(option = "A", direction = -1) +
  coord_fixed() +
  guides(fill = FALSE) +
  theme_paper
```

Plot heatmap per quarter:
```{r, fig.width = 12, fig.height = 16}
ggplot(trials_by_wday_hour, aes(x = hour, y = reorder(weekday, dplyr::desc(weekday)), fill = trials_normalised_quarter)) + 
  facet_grid(quarter ~ method) +
  geom_tile(colour = "white", size = 0.25) +
  labs(x = NULL,
       y = NULL,
       title = "Activiteit per uur gedurende de week",
       caption = "Aantal trials per weekdag en uur in elk kwartaal, genormaliseerd per methode.") +
  scale_x_continuous(expand = c(0,0), breaks = seq(0, 24, 3)) +
  scale_y_discrete(expand = c(0,0)) + 
  scale_fill_viridis_c(option = "A", direction = -1) +
  coord_fixed() +
  guides(fill = FALSE) +
  theme_bw(base_size = 16)
```

Plot heatmap for the period in which schools were closed:
```{r}
trials_closed <- unique(trials_by_wday_hour[schools_closed == TRUE, .(course, school_year, weekday, hour, trials_closed)])

trials_closed[, trials_normalised_closed := trials_closed / sum(trials_closed), by = .(course, school_year)]
```

```{r}
trials_closed_diff <- trials_closed[, .(school_year = "Change",
                                        trials_closed = trials_closed[school_year == "19/20"] - trials_closed[school_year == "18/19"],
                                        trials_normalised_closed = trials_normalised_closed[school_year == "19/20"] - trials_normalised_closed[school_year == "18/19"]), by = .(course, weekday, hour)]
```


```{r}
p_heatmap <- ggplot(trials_closed[course %in% c("English", "French"),],
       aes(x = hour, y = reorder(weekday, dplyr::desc(weekday)), fill = trials_normalised_closed)) + 
  facet_grid(school_year ~ course) +
  geom_tile(colour = "white", size = 0.25) +
  labs(x = "Time of day (hour)",
       y = NULL,
       fill = NULL) +
  scale_x_continuous(expand = c(0,0), breaks = seq(0, 24, 3)) +
  scale_y_discrete(expand = c(0,0)) + 
  scale_fill_viridis_c(option = "A", direction = -1) +
  coord_fixed() +
  theme_paper


p_heatmap
```


Make a plot of the difference between the two school years during the school closure period:
```{r}
p_heatmap_diff <- ggplot(trials_closed_diff[course %in% c("English", "French"),],
       aes(x = hour, y = reorder(weekday, dplyr::desc(weekday)), fill = trials_normalised_closed)) + 
  facet_grid(school_year ~ course) +
  geom_tile(colour = "white", size = 0.25) +
  labs(x = "Time of day (hour)",
       y = NULL,
       fill = NULL) +
  scale_x_continuous(expand = c(0,0), breaks = seq(0, 24, 3)) +
  scale_y_discrete(expand = c(0,0)) + 
  scale_fill_distiller(type = "div", palette = "RdBu", direction = -1, limits = c(-1, 1) * max(abs(trials_closed_diff[course %in% c("English", "French"),]$trials_normalised_closed))) +
  coord_fixed() +
  theme_paper

p_heatmap_diff
```


Make a combined plot for in the paper:
```{r}
p_heatmap_legend <- get_legend(p_heatmap)
p_heatmap_diff_legend <- get_legend(p_heatmap_diff)

p_heatmap <- p_heatmap + guides(fill = FALSE)
p_heatmap_diff <- p_heatmap_diff + guides(fill = FALSE)
```

```{r}
plot_grid(
  plot_grid(p_heatmap, p_heatmap_diff,
          ncol = 1,
          labels = c("A", "B"),
          rel_heights = c(1, .655)
          ),
  plot_grid(p_heatmap_legend, p_heatmap_diff_legend,
            ncol = 1,
            align = "vh", axis = "lrtb"),
  ncol = 2,
  rel_widths = c(1, .15))

ggsave("../output/combi_heatmap.pdf", width = 9, height = 5)
ggsave("../output/combi_heatmap.png", width = 9, height = 5)
```


## Activity stratified by year and level

```{r}
db <- db_connect()
counts_strat <- dbGetQuery(db,"SELECT responses.method AS 'method',
                          responses.book_info_id as 'book_info_id',
                          DATE(responses.date + 3600, 'unixepoch') AS 'doy',
                          responses.user_id AS 'user',
                          COUNT(*) AS 'trials'
                          FROM 'responses'
                          GROUP BY responses.method, responses.book_info_id,
                          DATE(responses.date + 3600, 'unixepoch'),
                          responses.user_id
                        ")
db_disconnect(db)

setDT(counts_strat)
```

```{r}
db <- db_connect()
book_info <- dbGetQuery(db, "SELECT * FROM 'book_info'")
db_disconnect(db)

setDT(book_info)
```

Add book information:
```{r}
counts_strat[book_info, on = "book_info_id", c("book_title", "method_group") := .(i.book_title, i.method_group)]
```

Add a school year column (cutoff date: 1 August):
```{r}
counts_strat[, doy_posix := as.POSIXct(doy)]
counts_strat[, school_year := ifelse(doy_posix < "2019-08-01", "18/19", "19/20")]
```

Add sensible course names:
```{r}
counts_strat[, course := ifelse(method == "Grandes Lignes", "French", ifelse(method == "Stepping Stones", "English", "German"))]
```

Count trials by day:
```{r}
counts_strat_by_day <- counts_strat[, .(trials_total = sum(trials, na.rm = TRUE)), by = .(school_year, course, method_group, book_title, doy_posix)]
setorder(counts_strat_by_day, school_year, course, method_group, book_title, doy_posix)
```

Simplify level names:
```{r}
# Keep all distinctions
counts_strat_by_day[, book_title_simple := stringr::str_sub(book_title, 3, -10)]
counts_strat_by_day[, book_title_simple := factor(book_title_simple, levels = c("vmbo b/lwoo", "vmbo b", "vmbo bk", "vmbo k", "vmbo kgt", "vmbo-gt", "vmbo gt", "vmbo-gt/havo", "vmbo (t)hv", "havo", "havo vwo", "vwo"))]

# Simplify to three levels
counts_strat_by_day[, level := dplyr::case_when(
  grepl("vmbo", book_title) ~ "Pre-vocational\n(vmbo)",
  grepl("havo", book_title) ~ "General secondary\n(havo)",
  grepl("vwo", book_title) ~ "Pre-university\n(vwo)",
  TRUE ~ "Other")]
counts_strat_by_day[, level := factor(level, levels = c("Other", "Pre-vocational\n(vmbo)", "General secondary\n(havo)", "Pre-university\n(vwo)"))]
```

Simplify year names:
```{r}
counts_strat_by_day[, year := dplyr::case_when(
  method_group == "Leerjaar 1 (5e Ed.)" ~ "Year 1",
  method_group == "Leerjaar 2 (5e Ed.)" ~ "Year 2",
  method_group == "Leerjaar 3 (5e Ed.)" ~ "Year 3",
  method_group == "Leerjaar 3/4 (5e Ed.)" ~ "Year 3/4",
  method_group == "Leerjaar 4 (5e Ed.)" ~ "Year 4",
  method_group == "Tweede Fase (6e Ed.)" ~ "Tweede Fase",
  TRUE ~ "Other")]
```


Align school years:
```{r}
counts_strat_by_day[school_year == "18/19", doy_posix_aligned := as.POSIXct(doy_posix + 365*24*60*60, origin = "1970-01-01")]
counts_strat_by_day[school_year == "19/20", doy_posix_aligned := doy_posix]
```

Use cut.Date() to bin dates by week. Each day is assigned the date of the most recent Monday.
```{r}
counts_strat_by_day[, doy_posix_aligned_week := cut.POSIXt(doy_posix_aligned, "week")]
counts_strat_by_day[, trials_total_week := sum(trials_total, na.rm = TRUE), by = .(school_year, course, method_group, book_title_simple, doy_posix_aligned_week)]

counts_strat_by_day[, trials_total_week_level := sum(trials_total), by = .(school_year, course, method_group, level, doy_posix_aligned_week)]
```


Summarise increase during lockdown:
```{r}
counts_strat_increase <- counts_strat_by_day[between(doy_posix_aligned, date_schools_closed, date_schools_opened), .(trials_lockdown = sum(trials_total)), by = .(course, book_title_simple, method_group, year, school_year)]
counts_strat_increase[, increase := trials_lockdown[2]/trials_lockdown[1], by = .(course, book_title_simple, method_group, year)]
counts_strat_increase[, increase_pct := paste0("Change:\n", scales::percent(increase, accuracy = 2))]

counts_strat_increase_level <- counts_strat_by_day[between(doy_posix_aligned, date_schools_closed, date_schools_opened), .(trials_lockdown = sum(trials_total)), by = .(course, level, method_group, year, school_year)]
counts_strat_increase_level[, increase := trials_lockdown[2]/trials_lockdown[1], by = .(course, level, method_group, year)]
counts_strat_increase_level[, increase_pct := paste0("Change:\n", scales::percent(increase, accuracy = 2))]
```


### French
```{r}
ggplot(counts_strat_by_day[course == "French"], 
       aes(group = school_year, colour = school_year, fill = school_year)) +
  facet_grid(book_title_simple ~ method_group) +
  geom_rect(xmin = date_schools_closed, xmax = date_schools_opened, ymin = -2e5, ymax = 2.2e6, fill = "grey92", colour = "grey50", lty = 2) +
  geom_ribbon(aes(x = doy_posix_aligned, ymin = 0, ymax = trials_total_week, ), alpha = .2) +
  geom_text(data = counts_strat_increase[course == "French" & school_year == "19/20"], 
            aes(label = increase_pct),
            x = as.POSIXct((as.numeric(date_schools_closed) + as.numeric(date_schools_opened))/2, origin = "1970-01-01"),
            y = 3.6e5,
            colour = "black",
            vjust = 1,
            show.legend = FALSE) +
  scale_x_datetime(expand = c(0, 0), 
                   breaks = as.POSIXct(c(
                     "2019-09-01 02:00:00 CET",
                     "2019-11-01 02:00:00 CET",
                     "2020-01-01 02:00:00 CET",
                     "2020-03-01 02:00:00 CET",
                     "2020-05-01 02:00:00 CET",
                     "2020-07-01 02:00:00 CET")),
                   limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
                   date_labels = "%b") +
  scale_y_continuous(expand = c(0, 0), limits = c(0, 3.75e5), labels = number_format) +
  scale_colour_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
  scale_fill_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
  labs(x = NULL,
       y = "Trials per week",
       colour = "School year",
       fill = "School year",
       title = "French") +
  theme_paper

ggsave("../output/trial_hist_french.pdf", width = 14, height = 10)
ggsave("../output/trial_hist_french.png", width = 14, height = 10)
```

Streamlined version for in the paper:
```{r}
ggplot(counts_strat_by_day[course == "French"], 
       aes(group = school_year, colour = school_year, fill = school_year)) +
  facet_grid(level ~ year) +
  geom_rect(xmin = date_schools_closed, xmax = date_schools_opened, ymin = -2e5, ymax = 2.2e6, fill = "grey92", colour = "grey50", lty = 2) +
  geom_ribbon(aes(x = doy_posix_aligned, ymin = 0, ymax = trials_total_week_level, ), alpha = .2) +
  geom_text(data = counts_strat_increase_level[course == "French" & school_year == "19/20"], 
            aes(label = increase_pct),
            x = as.POSIXct((as.numeric(date_schools_closed) + as.numeric(date_schools_opened))/2, origin = "1970-01-01"),
            y = 3.6e5,
            colour = "black",
            vjust = 1,
            size = rel(2.75),
            show.legend = FALSE) +
  scale_x_datetime(expand = c(0, 0), 
                   breaks = as.POSIXct(c(
                     "2019-10-01 02:00:00 CET",
                     "2019-12-01 02:00:00 CET",
                     "2020-02-01 02:00:00 CET",
                     "2020-04-01 02:00:00 CET",
                     "2020-06-01 02:00:00 CET")),
                   limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
                   date_labels = "%b") +
  scale_y_continuous(expand = c(0, 0), limits = c(0, 3.75e5), labels = number_format) +
  scale_colour_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
  scale_fill_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
  labs(x = NULL,
       y = "Trials per week",
       colour = "School year",
       fill = "School year") +
  theme_paper

ggsave("../output/trial_hist_french_level.pdf", width = 9, height = 5)
ggsave("../output/trial_hist_french_level.png", width = 9, height = 5)
```

### English

```{r}
ggplot(counts_strat_by_day[course == "English"], 
       aes(group = school_year, colour = school_year, fill = school_year)) +
  facet_grid(book_title_simple ~ method_group) +
  geom_rect(xmin = date_schools_closed, xmax = date_schools_opened, ymin = -2e5, ymax = 2.2e6, fill = "grey92", colour = "grey50", lty = 2) +
  geom_ribbon(aes(x = doy_posix_aligned, ymin = 0, ymax = trials_total_week, ), alpha = .2) +
  geom_text(data = counts_strat_increase[course == "English" & school_year == "19/20"], 
            aes(label = increase_pct),
            x = as.POSIXct((as.numeric(date_schools_closed) + as.numeric(date_schools_opened))/2, origin = "1970-01-01"),
            y = 3.6e5,
            colour = "black",
            vjust = 1,
            show.legend = FALSE) +
  scale_x_datetime(expand = c(0, 0), 
                   breaks = as.POSIXct(c(
                     "2019-09-01 02:00:00 CET",
                     "2019-11-01 02:00:00 CET",
                     "2020-01-01 02:00:00 CET",
                     "2020-03-01 02:00:00 CET",
                     "2020-05-01 02:00:00 CET",
                     "2020-07-01 02:00:00 CET")),
                   limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
                   date_labels = "%b") +
  scale_y_continuous(expand = c(0, 0), limits = c(0, 3.75e5), labels = number_format) +
  scale_colour_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
  scale_fill_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
  labs(x = NULL,
       y = "Trials per week",
       colour = "School year",
       fill = "School year",
       title = "English") +
  theme_paper

ggsave("../output/trial_hist_english.pdf", width = 14, height = 10)
ggsave("../output/trial_hist_english.png", width = 14, height = 10)
```

Streamlined version for in the paper:
```{r}
ggplot(counts_strat_by_day[course == "English" & level != "Other"], 
       aes(group = school_year, colour = school_year, fill = school_year)) +
  facet_grid(level ~ year) +
  geom_rect(xmin = date_schools_closed, xmax = date_schools_opened, ymin = -2e5, ymax = 2.2e6, fill = "grey92", colour = "grey50", lty = 2) +
  geom_ribbon(aes(x = doy_posix_aligned, ymin = 0, ymax = trials_total_week_level, ), alpha = .2) +
  geom_text(data = counts_strat_increase_level[course == "English" & level != "Other" & school_year == "19/20"], 
            aes(label = increase_pct),
            x = as.POSIXct((as.numeric(date_schools_closed) + as.numeric(date_schools_opened))/2, origin = "1970-01-01"),
            y = 9.6e5,
            colour = "black",
            vjust = 1,
            size = rel(2.75),
            show.legend = FALSE) +
  scale_x_datetime(expand = c(0, 0), 
                   breaks = as.POSIXct(c(
                     "2019-10-01 02:00:00 CET",
                     "2019-12-01 02:00:00 CET",
                     "2020-02-01 02:00:00 CET",
                     "2020-04-01 02:00:00 CET",
                     "2020-06-01 02:00:00 CET")),
                   limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
                   date_labels = "%b") +
  scale_y_continuous(expand = c(0, 0), limits = c(0, 1e6), labels = number_format) +
  scale_colour_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
  scale_fill_viridis_d(end = .5, direction = -1, na.translate = FALSE) +
  labs(x = NULL,
       y = "Trials per week",
       colour = "School year",
       fill = "School year") +
  theme_paper

ggsave("../output/trial_hist_english_level.pdf", width = 9, height = 5)
ggsave("../output/trial_hist_english_level.png", width = 9, height = 5)
```



## Question type

There are different question formats: open-answer, in which the student types the answer, and multiple-choice, in which the student selects the answer from a set of 3 or 4 options.

```{r}
db <- db_connect()
question_type <- dbGetQuery(db, 
                      "SELECT r.method AS 'method',
                      DATE(r.date + 3600, 'unixepoch') AS 'doy',
                      r.choices AS 'choices',
                      COUNT(*) AS 'n'
                      FROM 'responses' r
                      WHERE r.study == 0
                      GROUP BY r.method,
                      DATE(r.date + 3600, 'unixepoch'),
                      r.choices"
)
setDT(question_type)
db_disconnect(db)
```

Add a school year column (cutoff date: 1 August):
```{r}
question_type[, doy_posix := as.POSIXct(doy)]
question_type[, school_year := ifelse(doy_posix < "2019-08-01", "18/19", "19/20")]
```

Add sensible course names:
```{r}
question_type[, course := ifelse(method == "Grandes Lignes", "French", ifelse(method == "Stepping Stones", "English", "German"))]
```

Align school years:
```{r}
question_type[school_year == "18/19", doy_posix_aligned := as.POSIXct(doy_posix + 365*24*60*60, origin = "1970-01-01")]
question_type[school_year == "19/20", doy_posix_aligned := doy_posix]
```

Use cut.Date() to bin dates by week. Each day is assigned the date of the most recent Monday.
```{r}
question_type[, doy_posix_week := cut.POSIXt(doy_posix, "week")]
question_type[, doy_posix_aligned_week := cut.POSIXt(doy_posix_aligned, "week")]
```

```{r}
question_type_by_week <- question_type[, .(n = sum(n)), by = .(course, school_year, doy_posix_aligned_week, choices)]
```

```{r}
ggplot(question_type_by_week[course %in% c("English", "French")], aes(x = as.POSIXct(doy_posix_aligned_week), y = n, group = interaction(school_year,as.factor(choices)), colour = school_year)) +
  facet_grid(course ~ choices) +
  geom_line() +
  scale_x_datetime(expand = c(0, 0),
                   breaks = as.POSIXct(c(
                     "2019-10-01 02:00:00 CET",
                     "2019-12-01 02:00:00 CET",
                     "2020-02-01 02:00:00 CET",
                     "2020-04-01 02:00:00 CET",
                     "2020-06-01 02:00:00 CET")),
                   limits = as.POSIXct(c("2019-09-01 02:00:00 CET", "2020-07-01 02:00:00 CET")),
                   date_labels = "%b") +
  labs(x = NULL,
       y = "Trials",
       colour = "School year") +
  theme_paper

```

```{r}
question_type[, .(n = sum(n)), by = .(course, mcq = choices>1, school_year)][, .(perc_mcq = n[mcq == TRUE]/sum(n)), by = .(course, school_year)]
```

There is a clear difference between the languages in the question format used: English uses almost exclusively 4-alternative MCQs, while French uses a mix of MCQs (including a small number of 3-alternative questions) and open-answer questions.


# Session info
```{r}
sessionInfo()
```

